Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

Multiscale calculations reveal new insights into the reaction mechanism between KRASG12C and α, ß-unsaturated carbonyl of covalent inhibitors.

Utilizing α,ß-unsaturated carbonyl group as Michael acceptors to react with thiols represents a successful strategy for developing KRASG12C inhibitors. Despite this, the precise reaction mechanism between KRASG12C and covalent inhibitors remains a subject of debate, primarily due to the absence of an appropriate residue capable of deprotonating the cysteine thiol as a base. To uncover this reaction mechanism, we first discussed the chemical reaction mechanism in solvent conditions via density functional theory (DFT) calculation. Based on this, we then proposed and validated the enzymatic reaction mechanism by employing quantum mechanics/molecular mechanics (QM/MM) calculation. Our QM/MM analysis suggests that, in biological conditions, proton transfer and nucleophilic addition may proceed through a concerted process to form an enolate intermediate, bypassing the need for a base catalyst. This proposed mechanism differs from previous findings. Following the formation of the enolate intermediate, solvent-assisted tautomerization results in the final product. Our calculations indicate that solvent-assisted tautomerization is the rate-limiting step in the catalytic cycle under biological conditions. On the basis of this reaction mechanism, the calculated kinact/ki for two inhibitors is consistent well with the experimental results. Our findings provide new insights into the reaction mechanism between the cysteine of KRASG12C and the covalent inhibitors and may provide valuable information for designing effective covalent inhibitors targeting KRASG12C and other similar targets.

Synergistic inhibition mechanism of quinazolinone and piperacillin on penicillin-binding protein 2a: a promising approach for combating methicillin-resistant Staphylococcus aureus.

The production of penicillin-binding protein 2a (PBP2a), a cell wall synthesis protein, is primarily responsible for the high-level resistance observed in methicillin-resistant Staphylococcus aureus (MRSA). PBP2a exhibits a significantly reduced affinity for most ß-lactam antibiotics owing to its tightly closed active site. Quinazolinones (QNE), a novel class of non-ß-lactam antibiotics, could initiate the allosteric regulation of PBP2a, resulting in the opening of the initially closed active pocket. Based on our previous study, we have a basic understanding of the dual-site inhibitor ceftaroline (CFT) induced allosteric regulation of PBP2a. However, there are still limitations in the knowledge of how combining medicines, QNE and piperacillin (PIP), induce the allosteric response of PBP2a and inhibit its function. Herein, molecular dynamics (MD) simulations were performed to elucidate the intricate mechanisms underlying the combination mode of QNE and PIP. Our study successfully captured the opening process of the active pocket upon the binding of the QNE at the allosteric site, which alters the signaling pathways with a favorable transmission to the active site. Subsequent docking experiments with different conformational states of the active pocket indicated that all three inhibitors, PIP, QNE, and CFT, exhibited higher docking scores and more favorable docking poses to the open active pocket. These findings reveal the implied mechanism of QNE-mediated allostery underlying combination therapy and provide novel insights into developing innovative therapeutic modalities against MRSA.Communicated by Ramaswamy H. Sarma.

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks.

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review.

Drug discovery is a complex and iterative process, making it ideal for using artificial intelligence (AI). This paper uses a bibliometric approach to reveal AI's trend and underlying structure in drug discovery (AIDD). A total of 4310 journal articles and reviews indexed in Scopus were analyzed, revealing that AIDD has been rapidly growing over the past two decades, with a significant increase after 2017. The United States, China, and the United Kingdom were the leading countries in research output, with academic institutions, particularly the Chinese Academy of Sciences and the University of Cambridge, being the most productive. In addition, industrial companies, including both pharmaceutical and high-tech ones, also made significant contributions. Additionally, this paper thoroughly discussed the evolution and research frontiers of AIDD, which were uncovered through co-occurrence analyses of keywords using VOSviewer. Our findings highlight that AIDD is an interdisciplinary and promising research field that has the potential to revolutionize drug discovery. The comprehensive overview provided here will be of significant interest to researchers, practitioners, and policy-makers in related fields. The results emphasize the need for continued investment and collaboration in AIDD to accelerate drug discovery, reduce costs, and improve patient outcomes.

Experiment and molecular dynamics simulations reveal proanthocyanidin B2 and B3 can inhibit prion aggregation by different mechanisms.

Prion diseases are a group of fatal neurodegenerative diseases caused by the misfolding and aggregation of prion protein (PrP), and the inhibition of PrP aggregation is one of the most effective therapeutic strategies. Proanthocyanidin B2 (PB2) and B3 (PB3), the effective natural antioxidants have been evaluated for the inhibition of amyloid-related protein aggregation. Since PrP has similar aggregation mechanism with other amyloid-related proteins, will PB2 and PB3 affect the aggregation of PrP? In this paper, experimental and molecular dynamics (MD) simulation methods were combined to investigate the influence of PB2 and PB3 on PrP aggregation. Thioflavin T assays showed PB2 and PB3 could inhibit PrP aggregation in a concentrate-dependent manner in vitro. To understand the underlying mechanism, we performed 400 ns all-atom MD simulations. The results suggested PB2 could stabilize the α2 C-terminus and the hydrophobic core of protein by stabilizing two important salt bridges R156-E196 and R156-D202, and consequently made global structure of protein more stable. Surprisingly, PB3 could not stabilize PrP, which may inhibit PrP aggregation through a different mechanism. Since dimerization is the first step of aggregation, will PB3 inhibit PrP aggregation by inhibiting the dimerization? To verify our assumption, we then explored the effect of PB3 on protein dimerization by performing 800 ns MD simulations. The results suggested PB3 could reduce the residue contacts and hydrogen bonds between two monomers, preventing dimerization process of PrP. The possible inhibition mechanism of PB2 and PB3 on PrP aggregation could provide useful information for drug development against prion diseases.Communicated by Ramaswamy H. Sarma.

Unraveling the mechanism of ceftaroline-induced allosteric regulation in penicillin-binding protein 2a: insights for novel antibiotic development against methicillin-resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) acquires high-level resistance against ß-lactam antibiotics by expressing penicillin-binding protein 2a (PBP2a). PBP2a is a cell wall-synthesizing protein whose closed active site exhibits a reduced binding affinity toward ß-lactam antibiotics. Ceftaroline (CFT), a fifth-generation cephalosporin, can effectively inhibit the PBP2a activity by binding to an allosteric site to trigger the active site opening, allowing a second CFT to access the active site. However, the essential mechanism behind the allosteric behavior of PBP2a remains unclear. Herein, computational simulations are employed to elucidate how CFT allosterically regulates the conformation and dynamics of the active site of PBP2a. While CFT stabilizes the allosteric domain surrounding it, it simultaneously enhances the dynamics of the catalytic domain. Specifically, the study successfully captured the opening process of the active pocket in the allosteric CFT-bound systems and discovered that CFT alters the potential signal-propagating pathways from the allosteric site to the active site. These findings reveal the implied mechanism of the CFT-mediated allostery in PBP2a and provide new insights into dual-site drug design or combination therapy against MRSA targeting PBP2a.

The causal association between obesity and gastric cancer and shared molecular signatures: a large-scale Mendelian randomization and multi-omics analysis.

Purpose: While observational studies have identified obesity as a potential risk factor for gastric cancer, the causality remains uncertain. This study aimed to evaluate the causal relationship between obesity and gastric cancer and identify the shared molecular signatures linking obesity to gastric cancer. Methods: A two-sample Mendelian randomization (MR) analysis was conducted using the GWAS data of body fat percentage (exposure, n = 331,117) and gastric cancer (outcome, n = 202,308). Bioinformatics and meta-analysis of multi-omics data were performed to identify key molecules mediating the causality. The meta-analysis of the plasma/serum proteome included 1,662 obese and 3,153 gastric cancer patients. Obesity and gastric cancer-associated genes were identified using seven common gene ontology databases. The transcriptomic data were obtained from TCGA and GEO databases. The Bioinformatic findings were clinically validated in plasma from 220 obese and 400 gastric cancer patients across two hospitals. Finally, structural-based virtual screening (SBVS) was performed to explore the potential FDA-approved drugs targeting the identified mediating molecules. Results: The MR analysis revealed a significant causal association between obesity and gastric cancer (IVW, OR = 1.37, 95% CI:1.12-1.69, P = 0.0028), without pleiotropy or heterogeneity. Bioinformatic and meta-analysis of multi-omics data revealed shared TNF, PI3K-AKT, and cytokine signaling dysregulation, with significant upregulation of AKT1, IL-6, and TNF. The clinical study confirmed widespread upregulation of systemic inflammatory markers in the plasma of both diseases. SBVS identified six novel potent AKT1 inhibitors, including the dietary supplement adenosine, representing a potentially preventive drug with low toxicity. Conclusion: Obesity causally increases gastric cancer, likely mediated by persistent AKT1/IL-6/TNF upregulation. As a potential AKT1 inhibitor, adenosine may mitigate the obesity-to-gastric cancer transition. These findings could inform preventive drug development to reduce gastric cancer risk in obesity.

scCURE identifies cell types responding to immunotherapy and enables outcome prediction.

A deep understanding of immunotherapy response/resistance mechanisms and a highly reliable therapy response prediction are vital for cancer treatment. Here, we developed scCURE (single-cell RNA sequencing [scRNA-seq] data-based Changed and Unchanged cell Recognition during immunotherapy). Based on Gaussian mixture modeling, Kullback-Leibler (KL) divergence, and mutual nearest-neighbors criteria, scCURE can faithfully discriminate between cells affected or unaffected by immunotherapy intervention. By conducting scCURE analyses in melanoma and breast cancer immunotherapy scRNA-seq data, we found that the baseline profiles of specific CD8+ T and macrophage cells (identified by scCURE) can determine the way in which tumor microenvironment immune cells respond to immunotherapy, e.g., antitumor immunity activation or de-activation; therefore, these cells could be predictive factors for treatment response. In this work, we demonstrated that the immunotherapy-associated cell-cell heterogeneities revealed by scCURE can be utilized to integrate the therapy response mechanism study and prediction model construction.

Discovery of novel and potent InhA direct inhibitors by ensemble docking-based virtual screening and biological assays.

Multidrug-resistant tuberculosis (MDR-TB) continues to spread worldwide and remains one of the leading causes of death among infectious diseases. The enoyl-acyl carrier protein reductase (InhA) belongs to FAS-II family and is essential for the formation of the Mycobacterium tuberculosis cell wall. Recent years, InhA direct inhibitors have been extensively studied to overcome MDR-TB. However, there are still no inhibitors that have entered clinical research. Here, the ensemble docking-based virtual screening along with biological assay were used to identify potent InhA direct inhibitors from Chembridge, Chemdiv, and Specs. Ultimately, 34 compounds were purchased and first assayed for the binding affinity, of which four compounds can bind InhA well with KD values ranging from 48.4 to 56.2 µM. Among them, compound 9,222,034 has the best inhibitory activity against InhA enzyme with an IC50 value of 18.05 µM. In addition, the molecular dynamic simulation and binding free energy calculation indicate that the identified compounds bind to InhA with "extended" conformation. Residue energy decomposition shows that residues such as Tyr158, Met161, and Met191 have higher energy contributions in the binding of compounds. By analyzing the binding modes, we found that these compounds can bind to a hydrophobic sub-pocket formed by residues Tyr158, Phe149, Ile215, Leu218, etc., resulting in extensive van der Waals interactions. In summary, this study proposed an efficient strategy for discovering InhA direct inhibitors through ensemble docking-based virtual screening, and finally identified four active compounds with new skeletons, which can provide valuable information for the discovery and optimization of InhA direct inhibitors.

MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.

The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI  $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.

Phase solubility diagrams and energy surface calculations support the solubility enhancement with low hygroscopicity of Bergenin: 4-Aminobenzamide (1: 1) cocrystal.

Herein, we reported a new bergenin: 4-aminobenzamide (BGN-4AM) cocrystal with significantly enhanced solubility and low hygroscopicity probed from two aspects such as phase solubility diagrams and theoretical calculations. Compared with anhydrous BGN, BGN-4AM solubilities in water and different buffer solutions (pH = 1.2, 4.5, 6.8) increase significantly. It is noted that BGN-4AM solubility in pH = 6.8 buffer solution presents 32.7 times higher than anhydrous BGN. Interestingly, BGN-4AM (0.31 ± 0.07%) showcases lower hygroscopicity than anhydrous BGN (9.31 ± 0.16%). The predicted and experimental solubilities agree with each other when considering solubility product (Ksp) and solution binding constant (K11) in phase solubility diagrams, indicating the solution complexes formation occurs. Further crystal surface-water interactions and Bravais, Friedel, Donnay-Harker (BFDH) analyses based on Density Functional Theory with dispersion correction (DFT-d) methods support the enhanced solubility. The water probe demonstrates an average interaction energy of -6.48 kcal/mol on the 002 plane of BGN-4AM, and only -5.47 kcal/mol on the 011 plane of BGN monohydrate. The lower lattice energy of BGN-4AM guarantees its lower hygroscopicity than BGN monohydrate. BGN-4AM with enhanced solubility and low hygroscopicity can be a potential candidate for further formulation development.

Effects of the Glass-Forming Ability and Annealing Conditions on Cocrystallization Behaviors via Rapid Solvent Removal: A Case Study of Voriconazole.

The kinetic entrapment of molecules in an amorphous phase is a common obstacle to cocrystal screening using rapid solvent removal, especially for drugs with a moderate or high glass-forming ability (GFA). The aim of this study was to elucidate the effects of the coformer's GFA and annealing conditions on the nature of amorphous phase transformation to the cocrystal counterpart. Attempts were made to cocrystallize voriconazole (VRC) with four structural analogues, namely fumaric acid (FUM), tartaric acid (TAR), malic acid (MAL), and maleic acid (MAE). The overall GFA of VRC binary systems increased with decreasing glass transition temperatures (Tgs) of these diacids, which appeared as a critical parameter for predicting the cocrystallization propensity such that a high-Tg coformer is more desirable. A new 1:1 VRC-TAR cocrystal was successfully produced via a supercooled-mediated re-cocrystallization process, and characterized by PXRD, DSC, and FTIR. The cocrystal purity against the annealing temperature displayed a bell-shaped curve, with a threshold at 40 °C. The isothermal phase purity improved with annealing and adhered to the Kolmogorov-Johnson-Mehl-Avrami kinetics. The superior dissolution behavior of the VRC-TAR cocrystal could minimize VRC precipitation upon gastric emptying. This study offers a simple but useful guide for efficient cocrystal screening based on the Tg of structurally similar coformers, annealing temperature, and time.

In Vitro Release Study of the Polymeric Drug Nanoparticles: Development and Validation of a Novel Method.

The in vitro release study is a critical test to assess the safety, efficacy, and quality of nanoparticle-based drug delivery systems, but there is no compendial or regulatory standard. The variety of testing methods makes direct comparison among different systems difficult. We herein proposed a novel sample and separate (SS) method by combining the United States Pharmacopeia (USP) apparatus II (paddle) with well-validated centrifugal ultrafiltration (CU) technique that efficiently separated the free drug from nanoparticles. Polymeric drug nanoparticles were prepared by using a four-stream multi-inlet vortex mixer with d-α-tocopheryl polyethylene glycol 1000 succinate as a stabilizer. Itraconazole, cholecalciferol, and flurbiprofen were selected to produce three different nanoparticles with particle size <100 nm. By comparing with the dialysis membrane (DM) method and the SS methods using syringe filters, this novel SS + CU technique was considered the most appropriate in terms of the accuracy and repeatability to provide the in vitro release kinetics of nanoparticles. Interestingly, the DM method appeared to misestimate the release kinetics of nanoparticles through separate mechanisms. This work offers a superior analytical technique for studying in vitro drug release from polymeric nanoparticles, which could benefit the future development of in vitro-in vivo correlation of polymeric nanoparticles.

X-Ray Diffraction and Theoretical Calculation-Supported Formation of Polymorphic Cocrystals Discovered Through Thermal Methods: A Case Study.

Polymorphism commonly exists in the preparation of cocrystals and has attracted widespread attention from both the pharmaceutical industry and academia. However, few studies have examined how to discover polymorphic cocrystals and their potential formation mechanism. In this study, we report the novel discovery of salicylic acid: 3-nitrobenzamide (SA-3NBZ) polymorphic cocrystals by thermal methods. The formation mechanism is elucidated based on theoretical calculations. SA-3NBZ polymorphic cocrystals with molar ratio of 1:1 and 2:2 were discovered using the combination of differential scanning calorimetry and hot stage microscopy. Single-crystal X-ray diffraction analysis confirmed this discovery. Density functional theory calculations corrected with dispersion were conducted to illustrate the energetic stabilization of SA polymorphic cocrystals. Compared with the starting materials, formation of the cocrystals at 1:1 and 2:2 present a weak stabilization with overall energy reduction of -0.01 and -0.05 eV/molecule, respectively. The calculated noncovalent interactions index further suggests that intralayer hydrogen bonds and van der Waals forces contribute to these weak interactions. The density functional theory calculations are in good agreement with the X-ray diffraction data. Hence, thermal analysis is a simple and reliable method to discover polymorphic cocrystals.

Enhanced topical penetration, system exposure and anti-psoriasis activity of two particle-sized, curcumin-loaded PLGA nanoparticles in hydrogel.

Psoriasis is an immune-mediated skin disorder, which is triggered by the aberrant activation of dendritic cells in skin. This activation is followed by the complex interaction between the immune cells in the skin and keratinocyte in the epidermis. To improve the conditions of poor aqueous solubility and chemical stability, overcome skin barriers, and enhance in vivo anti-psoriatic activity, curcumin (Cur) loaded poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) were fabricated and administered by topical route to treat imiquimod (IMQ)-induced psoriasis-like mouse model. Spherical Cur-NPs with the mean particle sizes of 50nm and 150nm, respectively, were fabricated using a multi-inlet vortex mixer system, with both exhibiting significantly stronger anti-proliferation effect than Cur solution on HaCaT cells in vitro. Psoriatic skin was utilized in the in vitro skin penetration studies, and the results demonstrated that more drugs penetrated through or accumulated in the skin when administered as the Cur-NPs-loaded hydrogel compared to the drug suspension loaded hydrogel. To compare the nanosizing effect of these Cur-NPs, the mice with IMQ-induced psoriasis-like skin disease were treated with blank gel, Cur gel, 50nm sized NPs gel, 150nm sized NPs gel or tracrolimus cream (positive control), respectively. The results indicated that Cur-NPs hydrogel has a superior performance to Cur hydrogel on the IMQ-induced psoriasis-like mouse model in terms of morphological evaluation, biomarkers at mRNA, and protein levels. In conclusion, encapsulation of Cur into PLGA NPs, particularly for NPs of 50nm, could facilitate lipophilic Cur's dispersion, sustained-release, accumulation, and penetration across the skin and into the blood circulation, which significantly improves anti-psoriasis activity in mice.

Topical Fish Oil Application Coupling with Therapeutic Ultrasound Improves Tendon Healing.

Fish oil has been shown to promote collagen synthesis, and hence, connective tissue healing. Therapeutic ultrasound is commonly used to treat soft tissue injuries. This study aimed to investigate the therapeutic effect of topical fish oil on the management of Achilles tendon rupture, comparing normal therapeutic ultrasound with a combination of ultrasound and fish oil. Eighty-five Sprague-Dawley rats underwent surgical hemitenotomy of the right medial Achilles tendon. The rats received daily treatment of either topical placebo ointment (control group [CON]), topical fish oil (FO), therapeutic ultrasound (US) or ultrasound with fish oil as the coupling medium (FU). The treatment started on post-surgical day 2 over a 2-wk or 4-wk period. On days 15 and 29, the rats were sacrificed and their Achilles tendons were tested for structural stiffness, ultimate tensile strength (UTS) and energy absorption capacity. At 2 wk, only US showed higher normalized UTS compared with CON (p < 0.05). At 4 wk, both US and FU demonstrated better UTS (p < 0.05), while both FO and FU had improved in structural stiffness (p < 0.05). Four wk of treatment with ultrasound using fish oil as coupling medium showed improvement in both structural stiffness and UTS (p < 0.05).

Resveratrol cocrystals with enhanced solubility and tabletability.

Two new 1:1 cocrystals of resveratrol (RES) with 4-aminobenzamide (RES-4ABZ) and isoniazid (RES-ISN) were synthesized by liquid assisted grinding (LAG) and rapid solvent removal (RSR) methods using ethanol as solvent. Their physiochemical properties were characterized using PXRD, DSC, solid state and solution NMR, FT-IR, and HPLC. Pharmaceutically relevant properties, including tabletability, solubility, intrinsic dissolution rate, and hygroscopicity, were evaluated. Temperature-composition phase diagram for RES-ISN cocrystal system was constructed from DSC data. Both cocrystals show higher solubility than resveratrol over a broad range of pH. They are phase stable and non-hygroscopic even under high humidity conditions. Importantly, both cocrystals exhibit improved solubility and tabletability compared with RES, which make them more suitable candidates for tablet formulation development.

Identification of New Cocrystal Systems with Stoichiometric Diversity of Salicylic Acid Using Thermal Methods.

PURPOSE: The purpose of this work was to develop thermal methods to identify cocrystal systems with stoichiometric diversity. METHODS: Differential scanning calorimetry (DSC) and hot stage microscopy (HSM) have been applied to study the stoichiometric diversity phenomenon on cocrystal systems of the model compound salicylic acid (SA) with different coformers (CCFs). The DSC method was particularly useful in the identification of cocrystal re-crystallization, especially to improve the temperature resolution using a slower heating rate. HSM was implemented as a complementary protocol to confirm the DSC results. The crystal structures were elucidated by single-crystal X-ray diffraction (SXRD). RESULTS: Two new cocrystal systems consisting of salicylic acid-benzamide (SA-BZD, 1:1, 1:2) and salicylic acid-isonicotinamide (SA-ISN, 1:1, 2:1) have been identified in the present work. The chemical structures of the newly discovered cocrystals SA-BZD (1:2) and SA-ISN (2:1) have been elucidated using X-ray single crystal and powder diffraction methods. CONCLUSIONS: The developed thermal methods could rapidly identify cocrystal systems with stoichiometric diversity, with the potential to discover new pharmaceutical cocrystals in the future.

In silico prediction of prostaglandin D2 synthase inhibitors from herbal constituents for the treatment of hair loss.

ETHNOPHARMACOLOGICAL RELEVANCE: Many herbal topical formulations have been marketed worldwide to prevent hair loss or promote hair growth. Certain in vivo studies have shown promising results among them; however, the effectiveness of their bioactive constituents remains unknown. AIM OF THE STUDY: Recently, prostaglandin D2 (PGD2) inhibition has been discovered as a pharmacological mechanism for treating androgenic alopecia (AGA). This present study was aimed to identify prostaglandin D2 synthase (PTGDS) inhibitors in traditional Chinese medicines (TCMs) for treating AGA. MATERIALS AND METHODS: In this study, 389 constituents of 12 selected herbs were docked into 6 different crystal structures of PTGDS. The accuracy of the docking methods was successfully validated with experimental data from the ZINC In Man (Zim) database using receiver operating characteristic (ROC) studies. Seven essential drug properties were predicted for topical formulation: skin permeability, sensitisation, irritation, corrosion, mutagenicity, tumorigenicity and reproductive effects. RESULTS: Many constituents of the twelve herbs were found to have more advanced binding energies than the experimentally proved PTGDS inhibitors, but many of them were indicative of at least one type of skin adverse reactions, and exhibited poor skin permeability. CONCLUSIONS: Overall, ricinoleic acid, acteoside, amentoflavone, quercetin-3-O-rutinoside and hinokiflavone were predicted to be PTGDS inhibitors with good pharmacokinetic properties and minimal adverse skin reactions. These compounds have the highest potential for further in vitro and in vivo investigation with the aim of developing safe and high-efficacy hair loss treatment.